NanoJ: a high-performance open-source super-resolution microscopy toolbox

Romain F Laine , Kalina L Tosheva , Nils Gustafsson , Robert D M Gray , Pedro Almada , David Albrecht , Gabriel T Risa , Fredrik Hurtig , Ann-Christin Lindås , Buzz Baum , Jason Mercer , Christophe Leterrier (1) , Pedro M Pereira , Siân Culley , Ricardo Henriques

1 INP - Institut de neurophysiopathologie

Super-resolution microscopy (SRM) has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for SRM designed to combine high performance and ease of use. We named it NanoJ-a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.

Mots clés: super-resolution microscopy; ImageJ; Fiji; image analysis; image quality assessment; fluidics; single-particle analysis

Domaines: Neurobiologie

Romain F Laine, Kalina L Tosheva, Nils Gustafsson, Robert D M Gray, Pedro Almada, et al.. NanoJ: a high-performance open-source super-resolution microscopy toolbox. Journal of Physics D: Applied Physics, 2019, 52 (16), pp.163001. ⟨10.1088/1361-6463/ab0261⟩. ⟨hal-02073339⟩